Description: 基于em算法的图象分割程序,是图象分割的一类重要算法.-Em algorithm based on image segmentation process of image segmentation are essential for a class of algorithms. Platform: |
Size: 3072 |
Author:留云 |
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Description: 2008年3月
中国图象图形学报
基于类自适应高斯-马尔可夫随机场模型和EM 算法的MR图像分割
比较新的一片关于MARKOV以及EM算法的图像分割的文章。详细介绍了两种算法，以及对MR图像的实验结果，很有参考价值-March 2008 Journal of Image and Graphics of China based on the type of adaptive Gaussian- Markov random field model and the EM algorithm for MR image segmentation of a relatively new MARKOV as well as on the EM algorithm for image segmentation of the article. Two algorithms described in detail, as well as the experimental results of MR imaging is very useful Platform: |
Size: 268288 |
Author:luolunzi |
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Description: We present a method to learn and recognize object class
models from unlabeled and unsegmented cluttered scenes
in a scale invariant manner. Objects are modeled as flexible
constellations of parts. A probabilistic representation is
used for all aspects of the object: shape, appearance, occlusion
and relative scale. An entropy-based feature detector
is used to select regions and their scale within the image. In
learning the parameters of the scale-invariant object model
are estimated. This is done using expectation-maximization
in a maximum-likelihood setting. In recognition, this model
is used in a Bayesian manner to classify images. The flexible
nature of the model is demonstrated by excellent results
over a range of datasets including geometrically constrained
classes (e.g. faces, cars) and flexible objects (such
as animals). Platform: |
Size: 3409920 |
Author:Daria |
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Description: 了适应跟踪过程中目标光照条件的变化，并对目标特征进行在线更新，提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合，同时结合增量线性判别分析对目标进行跟踪的算法．跟踪开始前，为了获得比较准确的目标描述，使用混合高斯模型和期望最大化算法对目标进行分割；跟踪过程中，通过蒙特卡罗方法对目标区域和背景区域进行采样，并更新特征空间参数．得到目标和背景的最优分类面；最后使用粒子滤波器结合最优分类面对目标状态进行预测．通过光照变化的仿真视频和自然场景视频的跟踪实验，验证了文中算法的有效性．-Tracking process to adapt to changes in the target lighting conditions, and the target feature for online updates, proposes a local binary pattern (LBP) features and image intensity information integration, combined with incremental linear discriminant analysis for target tracking algorithms. Track begins, in order to obtain a more accurate description of the objectives, the use of Gaussian mixture models and expectation maximization algorithm for target segmentation tracking process, through the Monte Carlo method of the target area and the background area sampled and updated feature space parameters. Get the optimal target and background classification surface finally Using Particle Filter optimal classification predict the state of the face of goal. By varying illumination simulation video and natural scenes video tracking experiment to verify the effectiveness of the proposed algorithm. Platform: |
Size: 608256 |
Author:wenping |
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Description: 做动态图像的处理时，即做视频处理时，动态特征是不可或缺的，尤其是动态的期望和方差是我们必须使用的两个量，本程序提供了提取的算法-Do dynamic image processing, video processing is done, the dynamic characteristic is indispensable, especially in the dynamic expectation and variance is that we must use two quantities, the procedures provided extraction algorithm Platform: |
Size: 4794368 |
Author:郝伟杰 |
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Description: The computer science field has increased expectation technologies of computations based on Cellular Automata (CA) theory. In the area of image signal processing, several methods of Edge Detection are proposed, such as Canny-Deriche, Nalva, Iverson, Bergholm, Rothwell, Suzan, and others -The computer science field has increased expectation on technologies of computations based on Cellular Automata (CA) theory. In the area of image signal processing, several methods of Edge Detection are proposed, such as Canny-Deriche, Nalva, Iverson, Bergholm, Rothwell, Suzan, and others Platform: |
Size: 299008 |
Author:Shmail |
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Description: The digest of an image (either an image we have generated locally, or an image expectation).
Platform: |
Size: 5120 |
Author:mengunpi |
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Description: gaussian-mixture-models: This tool clusters the input image into n number of colored sections by synthesizing a Gaussian Mixture Model (GMM) using Expectation Maximization (EM). Platform: |
Size: 216064 |
Author:Chaojia |
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Description: EM 算法是求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。-Expectation Maximization image segmentation
Input:
ima: grey color image
k: Number of classes
Output:
mask: clasification image mask
mu: vector of class means
v: vector of class variances
p: vector of class proportions
Platform: |
Size: 1024 |
Author:lihaixing |
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Description: 种基于期望最大化( E M) 算法的局部图像特征的语义提取方法。首先提取图像的局部图像特 征， 统计特征在视觉词汇本中的出现频率， 将图像表示成词袋模型； 引入文本分析中的潜在语义分析技术建立从低层图像 特征到高层图像语义之间的映射模型； 然后利用 E M 算法拟合概率模型， 得到图像局部特征的潜在语义概率分布； 最后利 用该模型提取出的图像在潜在语义上的分布来进行图像分析和理解。-Semantic extraction of local image features based on expectation maximization (E M) algorithm. First extract the local features of the image, the visual vocabulary in the frequency of statistical feature, the image into the bag of words model introduce the latent semantic analysis of the text the technology to establish the mapping model between image low-level features to high-level semantic image and then use the E M algorithm for fitting probability model, probabilistic latent semantic distribution of local image features the distribution of the final image by using the model extracted in the latent semantic of image analysis and understanding. Platform: |
Size: 8817664 |
Author:杨雪 |
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Description: 本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.) Platform: |
Size: 1024 |
Author:xiaoxiaofish |
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Search - Expectation image